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Title: 【布客】面向机器学习的特征工程 中文翻译

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apachecn/fe4ml-zh https://github.com/apachecn/fe4ml-zh
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apachecn/fe4ml-zh https://github.com/apachecn/fe4ml-zh
面向机器学习的特征工程 中文文档 https://fe4ml.apachecn.org/
赞助我们 https://fe4ml.apachecn.org/#_2
一、引言 https://fe4ml.apachecn.org/1.%E5%BC%95%E8%A8%80/
二、简单数字的奇特技巧 https://fe4ml.apachecn.org/2.%E7%AE%80%E5%8D%95%E6%95%B0%E5%AD%97%E7%9A%84%E5%A5%87%E7%89%B9%E6%8A%80%E5%B7%A7/
三、文本数据：展开、过滤和分块 https://fe4ml.apachecn.org/3.%E6%96%87%E6%9C%AC%E6%95%B0%E6%8D%AE/
四、特征缩放的效果：从词袋到 TF-IDF https://fe4ml.apachecn.org/4.%E7%89%B9%E5%BE%81%E7%BC%A9%E6%94%BE%E7%9A%84%E6%95%88%E6%9E%9C%EF%BC%9A%E4%BB%8E%E8%AF%8D%E8%A2%8B%E5%88%B0_TF-IDF/
五、类别特征：机器鸡时代的鸡蛋计数 https://fe4ml.apachecn.org/5.%E7%B1%BB%E5%88%AB%E7%89%B9%E5%BE%81/
六、降维：使用 PCA 压缩数据集 https://fe4ml.apachecn.org/6.%E9%99%8D%E7%BB%B4%EF%BC%9A%E7%94%A8_PCA_%E5%8E%8B%E7%BC%A9%E6%95%B0%E6%8D%AE%E9%9B%86/
七、非线性特征提取和模型堆叠 https://fe4ml.apachecn.org/7.%E9%9D%9E%E7%BA%BF%E6%80%A7%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96%E5%92%8C%E6%A8%A1%E5%9E%8B%E5%A0%86%E5%8F%A0/
八、自动化特征提取器：图像特征提取和深度学习 https://fe4ml.apachecn.org/8.%E8%87%AA%E5%8A%A8%E5%8C%96%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96%E5%99%A8%EF%BC%9A%E5%9B%BE%E5%83%8F%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96%E5%92%8C%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/
附录、线性模型和线性代数基础 https://fe4ml.apachecn.org/%E9%99%84%E5%BD%95.%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B%E5%92%8C%E7%BA%BF%E6%80%A7%E4%BB%A3%E6%95%B0%E5%9F%BA%E7%A1%80/
面向机器学习的特征工程 中文文档 https://fe4ml.apachecn.org
利用 Python 进行数据分析 第二版 https://pyda.apachecn.org
Sklearn 与 TensorFlow 机器学习实用指南 第二版 https://hands1ml.apachecn.org
贡献者 https://fe4ml.apachecn.org/contrib/
关于我们 https://www.apachecn.org/about/
中文资源合集 https://docs.apachecn.org
赞助我们 https://fe4ml.apachecn.org/#_2
https://github.com/apachecn/fe4ml-zh/edit/master/docs/index.md
https://github.com/apachecn/fe4ml-zh/raw/master/docs/index.md
Feature Engineering for Machine Learning (Early Release)https://www.safaribooksonline.com/library/view/feature-engineering-for/9781491953235/
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